

import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= """https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
			a_						:						int 							= Image.open(requests.get(a__  ,	stream=a__).raw).convert("""RGB""")

			return image


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						str 							= []
			# fmt: off

			# vision encoder
			rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding"""))
			rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding"""))
			rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight"""))
			rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias"""))
			rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight"""))
			rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias"""))

			for i in range(config.vision_config.num_hidden_layers):
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',))
						rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight'''))
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias'''))

			# QFormer
			rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.embeddings.layernorm.weight"""))
			rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias"""))

			# fmt: on
			return rename_keys


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Dict 							= dct.pop(a__)
			a_						:						Dict 							= val


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for i in range(config.vision_config.num_hidden_layers):
						# read in original q and v biases
						a_						:						int 							= state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''')
						a_						:						Tuple 							= state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''')

						# next, set bias in the state dict
						a_						:						Optional[Any] 							= torch.cat((q_bias, torch.zeros_like(a__  ,	requires_grad=a__), v_bias))
						a_						:						List[str] 							= qkv_bias


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= 3_6_4 if """coco""" in model_name else 2_2_4
			a_						:						str 							= InstructBlipVisionConfig(image_size=a__).to_dict()

			# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
			# seems like flan-T5 models don't have bos_token_id properly set?
			if "t5-xl" in model_name:
						a_						:						List[Any] 							= TaConfig.from_pretrained("""google/flan-t5-xl"""  ,	dense_act_fn="""gelu"""  ,	bos_token_id=1).to_dict()
			elif "t5-xxl" in model_name:
						a_						:						Tuple 							= TaConfig.from_pretrained("""google/flan-t5-xxl"""  ,	dense_act_fn="""gelu"""  ,	bos_token_id=1).to_dict()
			elif "vicuna-7b" in model_name:
						a_						:						List[Any] 							= LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""  ,	vocab_size=3_2_0_0_1).to_dict()
			elif "vicuna-13b" in model_name:
						a_						:						Dict 							= LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""  ,	vocab_size=3_2_0_0_1).to_dict()
			else:
						raise ValueError("""Model name not supported""")

			# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
			a_						:						Optional[int] 							= InstructBlipQFormerConfig(vocab_size=3_0_5_2_3).to_dict()
			a_						:						Any 							= InstructBlipConfig(vision_config=a__  ,	text_config=a__  ,	qformer_config=a__)

			return config, image_size


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__=None  ,	a__=False):

			'''simple docstring'''



			a_						:						Optional[int] 							= AutoTokenizer.from_pretrained("""bert-base-uncased"""  ,	truncation_side="""left""")
			qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""})

			if "t5" in model_name:
						a_						:						List[Any] 							= TaTokenizerFast.from_pretrained("""google/flan-t5-xl"""  ,	truncation_side="""left""")
			elif "vicuna" in model_name:
						# the following was used in the original implementation:
						# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
						# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
						# tokenizer.add_special_tokens({"bos_token": "</s>"})
						# tokenizer.add_special_tokens({"eos_token": "</s>"})
						# tokenizer.add_special_tokens({"unk_token": "</s>"})
						a_						:						Any 							= LlamaTokenizerFast.from_pretrained(
						    """huggyllama/llama-7b"""  ,	truncation_side="""left"""  ,	bos_token="""</s>"""  ,	unk_token="""</s>""")
						tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""})

			a_							,					a_						:						Tuple 							= get_blipa_config(a__)
			a_						:						Dict 							= InstructBlipForConditionalGeneration(a__).eval()

			a_						:						int 							= {
			    """instructblip-vicuna-7b""": ("""blip2_vicuna_instruct""", """vicuna7b"""),
			    """instructblip-vicuna-13b""": ("""blip2_vicuna_instruct""", """vicuna13b"""),
			    """instructblip-flan-t5-xl""": ("""blip2_t5_instruct""", """flant5xl"""),
			    """instructblip-flan-t5-xxl""": ("""blip2_t5_instruct""", """flant5xxl"""),
			}

			a_							,					a_						:						Tuple 							= model_name_to_original[model_name]

			# load original model
			print("""Loading original model...""")
			a_						:						Tuple 							= """cuda:1""" if torch.cuda.is_available() else """cpu"""
			a_						:						Optional[int] 							= """cuda:2""" if torch.cuda.is_available() else """cpu"""
			a_							,					a_							,					a_						:						int 							= load_model_and_preprocess(
			    name=a__  ,	model_type=a__  ,	is_eval=a__  ,	device=a__)
			original_model.eval()
			print("""Done!""")

			# update state dict keys
			a_						:						List[Any] 							= original_model.state_dict()
			a_						:						Union[str, Any] 							= create_rename_keys(a__)
			for src, dest in rename_keys:
						rename_key(a__  ,	a__  ,	a__)

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						a_						:						List[str] 							= state_dict.pop(a__)
						if key.startswith("""Qformer.bert"""):
									a_						:						Optional[int] 							= key.replace("""Qformer.bert"""  ,	"""qformer""")
						if "attention.self" in key:
									a_						:						str 							= key.replace("""self"""  ,	"""attention""")
						if "llm_proj" in key:
									a_						:						List[str] 							= key.replace("""llm_proj"""  ,	"""language_projection""")
						if "t5_proj" in key:
									a_						:						Optional[Any] 							= key.replace("""t5_proj"""  ,	"""language_projection""")
						if key.startswith("""llm_model"""):
									a_						:						Tuple 							= key.replace("""llm_model"""  ,	"""language_model""")
						if key.startswith("""t5"""):
									a_						:						Any 							= key.replace("""t5"""  ,	"""language""")
						a_						:						Optional[Any] 							= val

			# read in qv biases
			read_in_q_v_bias(a__  ,	a__)

			# note: weights get loaded in torch.float32 by default
			hf_model.load_state_dict(a__  ,	strict=a__)

			a_						:						Tuple 							= load_demo_image()
			a_						:						int 							= """What is unusual about this image?"""

			# create processor
			a_						:						Optional[Any] 							= BlipImageProcessor(
			    size={"""height""": image_size, """width""": image_size}  ,	image_mean=a__  ,	image_std=a__)
			a_						:						Optional[int] 							= InstructBlipProcessor(
			    image_processor=a__  ,	tokenizer=a__  ,	qformer_tokenizer=a__  ,	)
			a_						:						str 							= processor(images=a__  ,	text=a__  ,	return_tensors="""pt""").to(a__)

			# make sure processor creates exact same pixel values
			a_						:						Union[str, Any] 							= vis_processors["""eval"""](a__).unsqueeze(0).to(a__)
			a_						:						List[str] 							= inputs.pixel_values
			assert torch.allclose(original_pixel_values.to(pixel_values.device)  ,	a__)

			original_model.to(a__)
			hf_model.to(a__)
			with torch.no_grad():
						if "vicuna" in model_name:
									a_						:						Optional[int] 							= original_model({"""image""": original_pixel_values, """text_input""": [prompt]}).logits
									a_						:						int 							= hf_model(**a__).logits
						else:
									a_						:						int 							= original_model(
									    {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}).logits
									a_						:						Any 							= tokenizer("""\n"""  ,	return_tensors="""pt""").input_ids.to(a__)
									a_						:						Tuple 							= label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id  ,	-1_0_0)
									a_						:						Dict 							= hf_model(**a__  ,	labels=a__).logits

			print("""First values of original logits:"""  ,	original_logits[0, :3, :3])
			print("""First values of HF logits:"""  ,	logits[0, :3, :3])

			# assert values
			assert original_logits.shape == logits.shape
			a_						:						List[str] 							= 1e-4 if """vicuna""" in model_name else 1e-5
			assert torch.allclose(original_logits.to(logits.device)  ,	a__  ,	atol=a__)
			print("""Looks ok!""")

			print("""Generating with original model...""")
			a_						:						Any 							= original_model.generate({"""image""": original_pixel_values, """prompt""": prompt}  ,	num_beams=5)

			# important: we need to cast the weights of the HF model to the appropriate type
			print("""Generating with HF model...""")
			a_						:						Dict 							= hf_model.generate(
			    **a__  ,	do_sample=a__  ,	num_beams=5  ,	max_length=2_5_6  ,	min_length=1  ,	top_p=0.9  ,	repetition_penalty=1.5  ,	length_penalty=1.0  ,	temperature=1  ,	)
			if "vicuna" in model_name:
						# convert output id 0 to 2 (eos_token_id)
						# TODO add this in the generate method?
						a_						:						Union[str, Any] 							= 2
			print("""Original generation:"""  ,	a__)
			a_						:						Dict 							= processor.batch_decode(a__  ,	skip_special_tokens=a__)
			a_						:						List[str] 							= [text.strip() for text in output_text]
			print("""HF generation:"""  ,	a__)

			if pytorch_dump_folder_path is not None:
						processor.save_pretrained(a__)
						hf_model.save_pretrained(a__)

			if push_to_hub:
						processor.push_to_hub(f'''Salesforce/{model_name}''')
						hf_model.push_to_hub(f'''Salesforce/{model_name}''')


if __name__ == "__main__":
	__snake_case				:      Dict   		=			argparse.ArgumentParser()
	__snake_case				:      int   		=			[
	    """instructblip-vicuna-7b""",
	    """instructblip-vicuna-13b""",
	    """instructblip-flan-t5-xl""",
	    """instructblip-flan-t5-xxl""",
	]
	parser.add_argument(
	    """--model_name""",
	    default="""instructblip-flan-t5-xl""",
	    choices=choices,
	    type=str,
	    help="""Path to hf config.json of model to convert""",
	)
	parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether to push the model and processor to the hub after converting""",
	)

	__snake_case				:      int   		=			parser.parse_args()

	convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


